Abstract: We propose semiparametric methods for estimating the effect of a time-dependent covariate on pre-treatment survival. The observed data consist of a longitudinal sequence of measurements and a potentially censored survival time. The factor of interest is time-dependent and affects both survival and treatment assignment. Survival in the absence of treatment is of interest and is dependently censored by the receipt of treatment. Patients may be removed from consideration for treatment, temporarily or permanently. The proposed methods combine landmark analysis, partly conditional hazard regression, and Inverse Probability of Censoring Weighting. The resulting estimators are consistent and asymptotically normal. We evaluate finite-sample properties through simulation, then use the proposed procedures to model pre-transplant mortality among End-stage Liver Disease patients. This is joint work with Qi Gong.
